When 5G Meets Deep Learning: A Systematic Review - MDPI

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Article
When 5G Meets Deep Learning: A Systematic Review
Guto Leoni Santos 1 , Patricia Takako Endo 2,∗ , Djamel Sadok 1 and Judith Kelner 1
 1   Centro de Informática, Universidade Federal de Pernambuco, Recife 50670-901, Brazil;
     gls4@cin.ufpe.br (G.L.S.); jamel@gprt.ufpe.br (D.S.); jk@gprt.ufpe.br (J.K.)
 2   Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco,
     Recife 50100-010, Brazil
 *   Correspondence: patricia.endo@upe.br
                                                                                                      



 Received: 28 July 2020; Accepted: 20 August 2020; Published: 25 August 2020                          


 Abstract: This last decade, the amount of data exchanged on the Internet increased by over
 a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020.
 This phenomenon is mainly due to the evolution of high-speed broadband Internet and,
 more specifically, the popularization and wide spread use of smartphones and associated accessible
 data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology,
 there is continual improvement to its radio technology and architecture such as in the scope of the
 LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation
 of telecommunication (5G) is considered and is gaining considerable momentum from both industry
 and researchers. In addition, with the deployment of the Internet of Things (IoT) applications,
 smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has
 emerged with very diverging and technologically challenging design requirements. These include
 high mobile data volume per area, high number of devices connected per area, high data rates,
 longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are
 being developed to meet these new requirements, and each of these technologies brings its own design
 issues and challenges. In this context, deep learning models could be seen as one of the main tools that
 can be used to process monitoring data and automate decisions. As these models are able to extract
 relevant features from raw data (images, texts, and other types of unstructured data), the integration
 between 5G and DL looks promising and one that requires exploring. As main contribution, this paper
 presents a systematic review about how DL is being applied to solve some 5G issues. Differently from
 the current literature, we examine data from the last decade and the works that address diverse 5G
 specific problems, such as physical medium state estimation, network traffic prediction, user device
 location prediction, self network management, among others. We also discuss the main research
 challenges when using deep learning models in 5G scenarios and identify several issues that deserve
 further consideration.

 Keywords: the next generation of telecommunication (5G); deep learning; reinforcement learning;
 systematic review; cellular networks

1. Introduction
     According to Cisco, the global Internet traffic will reach around 30 GB per capita by 2021,
where more than 63% of this traffic is generated by wireless and mobile devices [1]. The new
generation of mobile communication system (5G) will deal with a massive number of connected
devices at base stations, a massive growth in the traffic volume, and a large range of applications with
different features and requirements. The heterogeneity of devices and applications makes infrastructure
management even more complex. For example, IoT devices require low-power connectivity,

Algorithms 2020, 13, 208; doi:10.3390/a13090208                                www.mdpi.com/journal/algorithms

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trains moving at 300 KM/h need a high-speed mobile connection, users at their home need fiber-like
broadband connectivity [2] whereas Industry 4.0 devices require ultra reliable low delay services.
Several underlying technologies have been put forward in order to support the above. Examples of
these include multiple-input multiple-output (MIMO), antenna beamforming [3], virtualized network
functions (VNFs) [4], and the use of tailored and well provisioned network slices [5].
      Some data based solutions can be used to manage 5G infrastructures. For instance, analysis of
dynamic mobile traffic can be used to predict the user location, which benefits handover
mechanisms [6]. Another example is the evaluation of historical physical channel data to predict the
channel state information, which is a complex problem to address analytically [7]. Another example
is the network slices allocation according to the user requirements, considering network status and
the resources available [2]. All these examples are based on data analysis. Some examples are based
on historical data analysis, used to predict some behavior, and others are based on the current state
of the environment, used to help during decision making process. These type of problems can be
addressed through machine learning techniques.
      However, the conventional machine learning approaches are limited to process natural data in
their raw form [8]. For many decades, constructing a machine learning system or a pattern-recognition
system required a considerable expert domain knowledge and careful engineering to design a
feature extractor. After this step, the raw data could be converted into a suitable representation
to be used as input to the learning system [9].
      In order to avoid the effort for creating a feature extractor or suffering possible mistakes in
the development process, techniques that automatically discover representations from the raw data
were developed. Over recent years, deep learning has outperformed conventional machine learning
techniques in several domains such as computer vision, natural language processing, and genomics [10].
According to [9], deep learning methods “are representation-learning methods with multiple levels
of representation, obtained by composing simple but non-linear modules that each transforms the representation
at one level (starting with the raw input) into a representation at a higher, slightly more abstract level”.
Therefore, several complex functions can be learned automatically through sufficient and successive
transformations from raw data.
      Similarly to many application domains, deep learning models can be used to address problems of
infrastructure management in 5G networks, such as radio and compute resource allocation, channel
state prediction, handover prediction, and so on. This paper presents a systematic review of the
literature in order to identify how deep learning has been used to solve problems in 5G environments.
      In [11], Ahmed et al. presented some works that applied deep learning and reinforcement
learning to address the problem of resource allocation in wireless networks. Many problems and
limitations related to resource allocation, such as throughput maximization, interference minimization,
and energy efficiency were examined. While the survey presented in [11] focused on the resource
allocation problem, in this paper, we offer a more general systematic review spanning the used different
deep learning models applied to 5G networks. We also cover other problems present in 5G networks,
that demand the use of different deep learning models.
      Recently, in [12], Zhang et al. presented an extensive survey about the usage of deep learning in
mobile wireless networks. Authors focused on how deep learning was used in mobile networks and
potential applications, while identifying the crossover between these areas. Although it is very related
to our work, Zhang et al. had a more general focus, addressing problems related to generic wireless
networks such as mobility analysis, wireless sensor networks (WSN) localization, WSN data analysis,
among others. Our systematic review is focused on 5G networks and their scenarios, applications,
and problems. The deep learning models proposed in the analyzed works deal with specific cellular
network problems such as channel state information, handover management, spectrum allocation.
The scenarios addressed in the works that we select are also related with 5G networks and influence
the deep learning-based solution proposed.

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     Differently, the existing work in the literature, our research identifies some of the main 5G
problems addressed by deep learning, highlights the specific types of suitable deep learning models
adopted in this context, and delineates the major open challenges when 5G networks meet deep
learning solutions.
     This paper is structured as follows: Section 2 an overview of the methodology adopted to guide
this literature review. The results of the review including descriptive and thematic analysis are
presented in Section 3. The paper concludes with a summary of the key findings and contributions of
the paper in Section 4.

2. Systematic Review
      In this paper, we based our systematic review on the protocol established in [13] with the purpose
of finding the works that addressed the usage of deep learning models in the 5G context. We describe
the methodology steps in the following subsections.

2.1. Activity 1: Identify the Need for the Review
    As discussed previously, both 5G and deep learning are technologies that have received
considerable and increasing attention in recent years. Deep learning has become a reality nowadays
due to the availability of powerful off-the-shelf hardware and the emergence of new processing
processing units such as GPUs. The research community has taken this opportunity to create several
public repositories of big data to use in the training and testing of the proposed intelligent models.
5G on the other hand, has a high market appeal as it promises to offer new advanced services that,
up until now, no other networking technology was able to offer. 5G importance is boosted by the
popularity and ubiquity of mobile, wearable, and IoT devices.

2.2. Activity 2: Define Research Questions
      The main goal of this work is to answer the following research questions:

•     RQ. 1: What are the main problems deep learning is being used to solve?
•     RQ. 2: What are the main learning types used to solve 5G problems (supervised, unsupervised,
      and reinforcement)?
•     RQ. 3: What are the main deep learning techniques used in 5G scenarios?
•     RQ. 4: How the data used to train the deep learning models is being gathered or generated?
•     RQ. 5: What are the main research outstanding challenges in 5G and deep learning field?

2.3. Activity 3: Define Search String
      The search string used to identify relevant literature was: (5G and “deep learning”). It is important
to limit the number of strings in order to keep the problem tractable and avoid cognitive overwhelming.

2.4. Activity 4: Define Sources of Research
    We considered the following databases as the main sources for our research: IEEE
Xplore (http://ieeexplore.ieee.org/Xplore/home.jsp), Science Direct (http://www.sciencedirect.
com/), ACM Digital Library (http://dl.acm.org/), and Springer Library (https://link.springer.com/).

2.5. Activity 5: Define Criteria for Inclusion and Exclusion
     With the purpose of limiting our scope to our main goal, we considered only papers published in
conferences and journals between 2009 and 2019. A selected paper must discuss the use of deep learning
in dealing with a 5G technological problem. Note that solutions based on traditional machine learning
(shallow learning) approaches were discarded.

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2.6. Activity 6: Identify Primary Studies
     The search returned 3, 192, 161, and 116 papers (472 in total) from ACM Digital Library,
Science Direct, Springer Library, and IEEE Xplore, respectively. We performed this search in early
November 2019. After reading all the 472 abstracts and using the cited criteria for inclusion or exclusion,
60 papers were selected for the ultimate evaluation. However, after reading the 60 papers, two papers
were discarded because they were considered as being out of scope of this research. Next, two others
were eliminated. The first paper was discarded because it was incomplete, and the second one was
removed due presenting several inconsistencies in its results. Therefore, a total of 56 papers were
selected for the for ultimate data extraction and evaluation (see Table A1 in Appendix A).

2.7. Activity 7: Extract Relevant Information
     After reading the 56 papers identified in Activity 6, the relevant information was extracted as it
attempted to answer some of the research questions presented in the Activity 2.

2.8. Activity 8: Present an Overview of the Studies
      An overview of all works will be presented in this activity (see Section 3), in order to classify and
clarify the conducted works according to our research questions presented in Activity 2.

2.9. Activity 9: Present the Results of the Research Questions
     Finally, an overview of the studies in deep learning as it is applied to 5G is produced. It will
discuss our findings and address our research questions stated in Activity 2 (see Section 3).

3. Results
      In this section, we present our answers for the research question formulated previously.

3.1. What are the Main Problems Deep Learning Is Being Used to Solve?
     In order to answer RQ. 1, this subsection presents an overview of the papers found in the
systematic review. We separated the papers according to the problem addressed as shown in
Figure 1. The identified problems can be categorized in three main layers: physical medium, network,
and application.
     At the physical level of the OSI reference model, we detected papers that addressed problems
related to channel state information (CSI) estimation, coding/decoding scheme representation,
fault detection, device prediction location, self interference, beamforming definition, radio
frequency characterization, multi user detection, and radio parameter definition.              At the
network level, the works addressed traffic prediction through deep learning models and
anomaly detection. Research on resource allocation can be related to the physical or network level.
Finally, at the application level, existing works proposed deep learning-based solutions for
application characterization.
     In the following subsections, we will describe the problems solved by deep learning models;
further details about the learning and the deep learning types used in the models will be presented in
Sections 3.2 and 3.3, respectively.

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                       Figure 1. The problems related to 5G addressed in the works examined.

3.1.1. Channel State Information Estimation
     CSI estimation is a common problem in wireless communication systems. It refers to the channel
properties of a communication link [7]. In a simplified way, these properties describe how the signal
will propagate from the transmitter to the receiver. Based on the CSI, the transmission can be adapted
according to the current channel conditions, in order to improve the whole communication. CSI is an
important factor in determining radio resource allocation, the type of modulation and coding schemes
to use, etc.
     Traditional CSI estimation techniques usually require high computation capability [14].
In addition, these techniques may not be suitable for 5G scenarios due to the complexity of the
new scenarios and the presence of different technologies (e.g., massive MIMO, orthogonal frequency
division multiplexing (OFDM), and millimeter-Wave), that impact the physical medium conditions [7].
Therefore, several authors have used deep learning models for CSI estimation. In our systematic review,
we came across five papers related to CSI estimation with deep learning.
     Three works proposed a deep learning-based solution focused on MIMO systems [15–17].
In MIMO systems both transmitter and receiver are equipped with an array of antennas. This is
a very important technology for 5G, offering multiple orders of spectral and energy efficiency gains in
comparison to LTE technologies [18]. Note that LTE uses MIMO but 5G takes this technology a notch
further as it adopts massive antenna configurations in what is known as massive MIMO.
     In [15], the authors adopted deep learning for decision-directed for channel estimation (DD-CE)
in MIMO systems, to avoid the Doppler rate estimation. Authors considered vehicular channels,
where the Doppler rate varies from one packet to another, making the CSI estimation difficult.
Therefore, the deep learning model was used to learn and estimate the MIMO fading channels
over different Doppler rates.

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     In [16], the authors proposed a combination of deep learning and superimposed code (SC)
techniques for channel state CSI feedback. The main goal is to estimate downlink CSI and detect user
data in the base stations.
     In [17], Jiang et al. presented some evaluations for CSI estimation using deep learning models in
three use cases. The first one focused on MIMO with multi users where the angular power spectrum
(APS) information is estimated using deep learning models; and the two other scenarios were (a) static
CSI estimation framework based on deep learning; and (b) a variant of the first scheme, but considering
time variation, i.e., a deep learning model is proposed to estimate the CSI through time.
     In [7], Luo et al. proposed an online CSI prediction taking into account relevant features that
affect the CSI of a radio link, such as frequency band, user location, time, temperature, humidity,
and weather.
     In [19], a residual network was proposed for CSI estimation in filter bank multicarrier (FBMC)
systems. The traditional CSI estimation and equalization and demapping module are replaced by deep
learning model.

3.1.2. Coding/Decoding Scheme Representation
     The generation of the information at the source and the reconstruction of such information
at the receiver makes up the coding and decoding processes, respectively. However, due to
the unstable nature of the channels, some disturbances and noise in the signal can cause data
corruption [20]. Considering the 5G networks, where new technologies, such as MIMO, non-orthogonal
multiple access (NOMA), mmWave will be deployed, the coding/decoding schemes must be adapted
to work properly. These schemes need to characterize several phenomena that can impact the
data transmission, such as signal diffraction, fading, path loss, and scattering.
     We identified a total of seven works that addressed the coding/decoding schemes using deep
learning models.
     Three of these considered NOMA technology using deep learning models.                    In [21],
the authors proposed a deep learning-based solution to parameterize the bit-to-symbol mapping
and multi-user detection. Recall that as we are using non orthogonal modulation, multi-user detection
becomes a cumbersome issue. In [22], the authors proposed a deep learning model to learn the
coding/decoding process of MIMO-NOMA system in order to minimize the total mean square error
of the users signals. In [23], the authors proposed a deep learning model to be used in sparse code
multiple access (SCMA) system, which is a promising code-based NOMA technique, with the goal to
minimize the bit error rate.
     The authors in [24] considered multiuser single-input multiple-output (MU-SIMO) systems.
A simple deep learning model was considered for joint multi user waveform design at the
transmitter side, and non coherent signal detection at the receiver side. The main goal was to reduce
the difference between the transmitted and received signals.
     In [25], Kim et al. proposed a novel peak-to-average power ratio (PAPR) reduction scheme using
deep learning of OFDM systems. The presence of large PAPR values is harmful to battery life as high
peaks tend to draw high levels of energy from sometimes energy limited devices. The model proposed
map and demap symbols on each subcarrier adaptively and both bit error rate (BER) and the PAPR of
the OFDM system could be jointly minimized.
     In [26], a deep learning based unified polar-low-density parity-check (LDPC) is proposed.
The deep learning model was created to receive the observed symbols and an additional information
introduced by the authors called “indicator section”, and to output the signal decoded.
     In [27], a coding mechanism under low latency constraints based on deep learning was proposed.
The idea was to create a robust and adaptable mechanism for generic codes for future communications.

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3.1.3. Fault Detection
     Fault detection systems are very important to achieving ultra-reliable low latency
communication (URLLC). For example, mission-critical industrial automation applications is a type of
application that demands stringent timing and reliability guarantees for data collection, transmission,
and processing [28]. Identifying faults is crucial to ensure low latency (since damaged equipment
may increase the time transmission) and reliable communication (since point of failure may reduce
the overall network performance). However, due to the device heterogeneity of 5G networks,
identifying faults is a complex task that requires sophisticated techniques in order to automate
such task.
     In this systematic review, we found two papers that addressed fault detection in 5G scenarios
using deep learning models.
     In [29], a deep-learning-based schema was proposed to detect and locate antenna faults in
mmWave systems. Firstly, the scheme detects the faults (using a simple neural network with a
low cost), and then it locates where the fault occurred. Since the second step is a more complex task
due to the high number of antennas present in a mmWave system, a more complex neural network
was proposed.
     In [30], Yu et al. covered fronthaul network faults. The model was designed to locate single-link
faults in 5G optical fronthaul networks. The proposed model was able to identify faults and false
alarms among alarm information considering single-link connections.

3.1.4. Device Location Prediction
     Unlike traditional networks, in telecommunication networks, the nodes are characterized
by a high mobility; and determining or estimating their mobility behavior is a complex task.
Device location prediction has many applications, such as location-based services, mobile access control,
mobile multimedia quality of service (QoS) provision, as well as the resource management for mobile
computation and storage [31].
     Considering urban scenarios, it is known that movement of people has a high degree of
repetition, because they visit regular places in the city such as their own homes and places of work.
These patterns can help to build services for specific places in order to increase user experience [32].
In addition, more detailed information about human mobility across the city can be collected using
smartphones [33]. This information (combined with other data sources) can be used as input for
models to estimate the device and consequently user location with high accuracy.
     In this systematic review, three articles presented models to deal with device location prediction.
Two works focused on device location prediction in mmWave systems [34,35]. In these systems,
predicting the device location is a complex task due to the radiation reflected on most visible objects,
which creates a rich multi path (interference) environment. In [34], a deep learning model was used
to predict user location based on the radiation sent by the obstacles encountered. These carry latent
information regarding their relative positions; while in [35], fingerprint historical data was used to
estimate the device location over beamformed fingerprints.
     In [36], the authors proposed a deep learning model to predict the device location in
ultra-dense networks. Predicting the device location in this scenario is important because the
deployment of small cells inevitably leads to more frequent handovers, making the mobility
process more challenging. The model was used to predict user mobility and anticipate the
handover preparation. The model was designed to estimate the future position of an user based
on her/his historical data. If a handover is estimated as being eminent, deep learning model was able
to determine the best base station to receive the user.

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3.1.5. Anomaly Detection
     Future 5G networks will lead with different types of devices over heterogeneous wireless networks
with higher data rates, lower latency and lower power consumption. Autonomous management
mechanisms will be needed to reduce the control and monitoring of these complex networks [37].
     Anomaly detection systems are important to identify malicious network flows that may impact
users and the network performance. However, developing these systems remains a considerable
challenge due to the large data volume generated in 5G systems [38,39].
     Four articles addressing the anomaly detection problem using deep learning in 5G were identified
in this systematic review. In [38,40], the authors deal with cyber security defense systems in
5G networks, proposing the use of deep learning models that are capable of extracting features
from network flows and the quick identification of cyber threats.
     In [10,41], the authors proposed a deep learning-based solution to detect anomalies in the
network traffic, considering two types of behavior as network anomalies: sleeping cells and
soared traffic. Sleeping cells can happen due to failures in the antenna hardware or random
access channel (RACH) failures due to RACH misconfiguration, while soared traffic can result in
network congestion, where traffic increases but with relatively smaller throughput to satisfy the
users’ demand. Recall that RACH is the channel responsible for giving users radio resources so
when RACH is not working properly we effectively have a sleepy cell with no transmission activity
taking place.

3.1.6. Traffic Prediction
     It is expected that Internet traffic will grow tenfold by 2027. This acts as a crucial anchor to create
the new generation of cellular network architecture [42]. Predicting traffic for the next day, hour,
or even the next minute can be used to optimize the available system resources, for example by
reducing the energy consumption, applying opportunistic scheduling, or preventing problems in the
infrastructure [42].
     In this systematic review, we found eight works that addressed traffic prediction using
deep learning.
     The works presented in [43,44] proposed a deep learning-based solution to predict traffic for
network slicing mechanisms. Note that 5G relies on the use of network slicing in order to accommodate
different services and tenants while virtually isolating them. In [43], a proactive network slice
mechanism was proposed and a deep learning model was used to predict the traffic with high accuracy.
In [44], a mechanism named DeepCog was proposed with a similar purpose. DeepCog can forecast the
capacity needed to allocate future traffic demands in network slices while minimizing service request
violations and resource overprovisioning.
     Three works considered both temporal and spatial dependence of cell traffic. In [6], the authors
proposed a deep learning model to predict citywide traffic. The proposed model was able to capture
the spatial dependency and two temporal dependencies: closeness and period. In [45], the authors
proposed different deep learning models for mobile Internet traffic prediction. The authors used
the different models to consider spatial and temporal aspects of the traffic. The maximum, average,
and minimum traffic were predicted for the proposed models. In [46], the authors proposed a deep
learning-based solution to allocate remote radio heads (RRHs) into baseband unit (BBU) pools in a
cloud radio access network (C-RAN) architecture. The deep learning model was used to predict traffic
demand of the RRHs considering the spatial and temporal aspects. The prediction was used to create
RRH clusters and map them to BBU pools in order to maximize the average BBU capacity utility and
minimize the overall deployment cost.
     In [47], the authors considered traffic prediction in ultra-dense networks, which is a complicated
scenario due to the presence of beamforming and massive MIMO technologies. A deep learning model
was used to predict the traffic in order to detect if a congestion will take place and then take decisions
to avoid/alleviate such congestion.

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     In [48], the authors addressed the benefits of cache offloading in small base stations considering
the mobile edge computing (MEC). The offloading decision is based on the users’ data rate, where the
users with low data rates are offloaded first. Consequently, the authors proposed a deep learning
model to predict the traffic data rate of the users in order to have a guide for the scheduling
offloading mechanism.

3.1.7. Handover Prediction
      The handover process ensures continuous data transfer when users are on the move between
call towers. For that, the mobile management entity (MME) must update the base stations where the
users are connected. This procedure is known as location update. The handover delay is one of the main
problems in wireless networks [49]. Conventionally, a handover is carried out based on a predefined
threshold of the Reference Signal Receiver Power (RSRP), the Reference Signal Receiver Quality (RSRQ),
among other signal strength parameters [50]. Predicting the handover based on the nearby stations’
parameters can be a fruitful strategy to avoid handover errors, temporary disconnections and improve
user experience [49].
      In this systematic review, we located two papers that addressed handover prediction. In [51],
Khunteta et al. proposed a deep learning model to avoid handover failures. For that, the deep learning
model was trained to detect if the handover will fail or be successful based on the historical signal
condition data.
      In [52], the handover prediction was tested to provide uninterrupted access to wireless
services without compromising the expected QoS. The authors proposed both analytical and deep
learning-based approaches to predict handover events in order to reduce the holistic cost.

3.1.8. Cache Optimization
      In the last decade, multimedia data became dominant in mobile data traffic. This raised additional
challenges in transporting the big volume of data from the content providers to the end users with
high-rates and low latency. The main bottleneck point is the severe traffic congestion observed in
the backhaul links, specially in 5G scenarios, where several small base stations will be scattered [53].
To mitigate this issue, the most popular content can be stored (cached) at the edge of the network
(e.g., in the base stations) in order to free backhaul link usage [54]. However, finding the best strategy
for the cache placement is a challenge. The best content to cache and the best location for storing this
content are both decisions that can impact the cache scheme performance.
      Two works addressed the cache placement problem in 5G environments using deep
learning models. In [55], authors proposed a collaborative cache mechanism in multiple RRHs
to multiple BBUs based on reinforcement learning. This approach was used because rule-based
and metaheuristics methods suffer some limitations and fail to consider all environmental factors.
Therefore, by using reinforcement learning, the best cache strategy can be selected in order to reduce
the transmission latency from the remote cloud and the traffic load of backhaul.
      In [56], the authors considered ultra-dense heterogeneous networks where the content cache
is performed at small base stations. The goal is to minimize energy consumption and reduce the
transmission delay, optimizing the whole cache placement process. Instead of using traditional
optimization algorithms, a deep learning model was trained to learn the best cache strategy. This model
reduces the computational complexity achieving a real time optimization.

3.1.9. Resource Allocation/Management
     As the numbers of users, services, and resources increase, the management and orchestration
complexity of resources also increase. The efficient usage of resources can be translated into cost
reduction and avoid over/under resource dimensioning. Fortunately, under such a very dynamic
and complex network environment, recent achievements in machine learning that interact with
surrounding environments can provide effective way to address these problems [57].

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      Four papers addressed resource allocation in network slices using solutions based on deep
learning [5,57–59]. A network slice is a very important technology for 5G since it will allow a network
operator to offer a diverse set of tailored and isolated services over a shared physical infrastructure.
      A deep learning-based solution was proposed in [58] to allocate slices in 5G networks. The authors
proposed a metric called REVA that measures the amount of Physical Resource Blocks (PRBs) available
to active bearers for each network slice, and a deep learning model was proposed to predict such metric.
      Yan et al. proposed a framework that combined deep learning and reinforcement learning to
resource scheduling and allocation [57]. The main goal was to minimize resource consumption at
the same time guaranteeing the required performance isolation degree by a network slice. In [5],
the authors proposed a framework for resource allocation in network slices and a deep learning model
was used to predict the network status based on historical data. In [59], a model was proposed to
predict the medium usage for network slices in 5G environments while meeting service level agreement
(SLA) requirements.
      Three papers proposed deep learning-based solutions to optimize the energy consumption in
5G networks [60–63]. The works proposed by [60,61] focused on NOMA systems. A framework was
proposed in [60] to optimize energy consumption. A deep learning model is part of the framework
and was used to map the input parameters (channel coefficients, the user demands, user power,
and the transmission deadline) into an optimal scheduling scheme. In [61], a similar strategy was used,
where a deep learning model was used to find the approximated optimal joint resource allocation
strategy to minimize energy consumption. In [62], a deep learning model was used in the MME for
user association taking into account the behavior of access points in the offloading scheme. In [63],
the authors proposed a deep learning model to allocate carriers in multi-carrier power amplifier
(MCPA) dynamically, taking into account the energy efficiency. The main idea was to minimize the
total power consumption while finding the optimal carrier to MPCA allocation. To solve this problem,
two approaches were used: convex relaxation and deep learning. The deep learning model was used
to approximate the power consumption function formulated in the optimization problem, since it is a
non-convex and non-continuous function.
      In [64], the authors proposed a deep learning-based solution for downlink coordinated multi-point
(CoMP) in 5G. The model receives physical layer measurements from the user equipment and
“formulates a modified CoMP trigger function to enhance the downlink capacity” [64]. The output of
the model is the decision to enable/disable the CoMP mechanism.
      In [65], the authors proposed a deep learning model for smart communication systems with
high density D2D mmWave environments using beamforming. The model selects the best relay node
taking into account multiple reliability metrics in order to maximize the average system throughput.
The authors in [11] also proposed a deep learning-based solution to maximize the network throughput
considering resource allocation in multi-cell networks. A deep learning model was proposed to
predict the resource allocation solution (taking as input the channel quality indicator and user location)
without intensive computations.

3.1.10. Application Characterization
     In cellular networks, self-organizing networks (SON) is a technology designed to plan, deploy,
operate, and optimize mobile radio access networks in a simple, fast, and automated way. SON is
a key technology for future cellular networks due to the potential of saving capital expenditure
(CAPEX) and operational expenditure (OPEX). However, SON is not only about network performance
but also QoS. A better planning of network resources can be translated into a better service quality and
increasing revenues.
     The authors in [66,67] presented a framework for self-optimization in 5G networks
called APP-SON. It was designed to optimize some target network key performance indicators (KPIs)
based on the mobile applications characteristics, by identifying similar application features and creating
clusters using the Hungarian Algorithm Assisted Clustering (HAAC). The homogeneous application

Algorithms 2020, 13, 208                                                                           11 of 34

characteristics of cells in a cluster are identified to prioritize target network KPIs in order to improve
user quality of experience (QoE). This is achieved through cell engineering parameters adjustments.
The deep learning model was used to establish cause effect between the cell engineering parameters
and the network KPIs. For instance, Video application KPIs can be used to detect that this type of
traffic occupies more than 90% of the total traffic, and thus adjust the cell engineering parameters to
give priority to video traffic.

3.1.11. Other Problems
     Some papers addressed problems which are not related to the ones previously listed.
Thus, we will describe them separately.
     The work presented in [68] applied a deep learning model to a massive MIMO system to
solve the pilot contamination problem [69]. The authors highlighted that conventional approaches
of pilot assignment are based on heuristics that are difficult to deploy in a real system due to
high complexity. The model was used to learn the relationship between the users’ location and
the near-optimal pilot assignment with low computational complexity, and consequently could be
used in real MIMO scenarios.
     The self-interference problem was addressed in [70]. A digital cancellation scheme based on
deep learning was proposed for full-duplex systems. The proposed model was able to discover the
relationship between the signal sent through the channel and the self-interference signal received.
The authors evaluated how the joint effects of non-linear distortion and linear multi-path channel
impact the performance of digital cancellation using the deep learning model.
     The authors in [71] represented the characterization of radio frequency (RF) power amplifiers
(PAs) using deep learning. While in previous works they have considered only linear aspects of PA,
the authors included non-linear aspects of PA taking into account memory aspects of deep learning
models in [71]. They defined the map between the digital base station stimulus and the response of PA
as a non-linear function. However, the conventional methods to solve this function require a designer
to extract the interest parameters for each input (base station stimulus) manually. As a result, a deep
learning model was proposed to represent this function, extracting the parameters automatically from
measured base station stimulus and giving as output the PA response.
     In [2], reinforcement learning was used to learn the optimal physical-layer control parameters
of different scenarios. Authors proposed a self-driving radio, which learns the near-optimal
control algorithm while taking int account the high-level design specifications provided by the
network designer. A deep learning model was proposed to map the network specifications into
physical-layer control instructions. This model was then used in the reinforcement learning algorithm
to take decisions according to feedback from the environment.
     In [72], the spectrum auction problem was addressed using deep learning. The idea was to allocate
spectrum among unlicensed users taking into account the interests of the channel for the auction,
and the interference suffered during communication as well as economic capability. A deep learning
model was proposed for spectrum auction, and it receives as input three factors: the interference,
experience, and economic ability; and gives as output a number between zero and one that determines
whether the channel will be allocated for a user or not.
     In [73], path scheduling in a multi path scenario was addressed using reinforcement learning.
In these systems, the traffic is distributed across the different paths according to policies,
packet traffic classes, and the performance of the available paths. Thus, reinforcement learning
was used to learn from the network the best approach for scheduling packets across the different paths.
     The security aspect of cooperative NOMA systems was considered in [74]. In cooperative NOMA,
the user with a better channel condition acts as a relay between the source and a user experiencing
poor channel conditions (user receiver). The security may be compromised in the presence of an
eavesdropper in the network. Therefore, a deep learning model was proposed to find the optimal power
allocation factor of a receiver in a communication system has the presence of an eavesdropper node.

Algorithms 2020, 13, 208                                                                         12 of 34

The model input data are the channel realization while the output are the power allocation factor of
the user with poor channel conditions.
     In [75], authors considered the propagation prediction using deep learning models. Predicting the
propagation characteristics accurately is needed for optimum cell design. Thus, the authors proposed a
deep learning model to learn propagation loss from the map of a geographical area with high accuracy.
     The authors in [76] considered the multiuser detection problem in an SCMA system. A deep
learning model was used to mimic the message passing algorithm (MPA), which is the most popular
approach to implement multiuser detection with low complexity. The deep learning model was
designed to estimate the probability that a user is assigned into a resource block from a pool of
resource blocks, taken the signal sent by the users as input.
     In [3], an intelligent beamforming technique based on MIMO technology was proposed using
reinforcement learning. The proposal builds a self-learning system to determine the phase shift and the
amplitude of each antenna. The reinforcement learning algorithm can adapt the signal concentration
based on the number of users located in a given area. If there are many users in a given small area,
the solution may produce a more targeted signal for users located at that area. However, if users are
spread out over a wide area, a signal with wide coverage will be sent to cover the entire area.
     In [77], Tsai et al. proposed a reinforcement learning-based solution in order to choose the best
configuration of uplink and downlink channels in dynamic time-division duplexing (TDD) systems.
The main goal was to optimize the mean opinion score (MOS), which is a QoE metric. This metric has
a direct relationship with the system throughput. The optimization problem was formulated as one
that maximizes the MOS of the system by allocating uplink and downlik traffic for the time frames.
Thus, a set of downlink and uplink configurations was defined by the authors and, for each frame,
these configurations are chosen for each base station.

3.2. What Are the Main Types of Learning Techniques Used to Solve 5G Problems?
     The works captured in this systematic review used three different learning techniques, as shown
in Figure 2. The majority of the these works used supervised learning (fifty articles), followed by
reinforcement learning (seven articles), and unsupervised learning (four articles only).

                  Figure 2. Most common learning type used in the deep learning models for 5G.

Algorithms 2020, 13, 208                                                                                      13 of 34

3.2.1. Supervised Learning
    Although it is hard to find labeled datasets in 5G scenarios, most of the papers used the
supervised learning approach. This approach is widely used for classification tasks (such as [78–81])
and regression problems (such as [82–85]), what are the most common problems addressed in the
works found in this systematic review.
    We classified the 50 articles that used supervised learning between classification and regression
problems as shown in Table 1. We can see that 32 articles addressed classification problems in
5G scenarios whereas 19 articles dealt with regression models.

                   Table 1. Articles that used supervised learning in their deep learning models.

Problem Type        Number of Articles                                   References
 Classification             32            [2,10,11,16,17,19,21–23,26,27,29,30,34,38,40,41,52,56,60–66,68,71,72,74–76]
  Regression                19                               [5–7,15,17,35,36,43–48,51,57–59,67,70]

3.2.2. Reinforcement Learning
     Reinforcement learning has received a lot of attention in the last years. This paradigm is based
on trial and error, where software agents learn a behavior that optimizes the reward observing
the consequences of their actions [86]. The works we reviewed addressed different problems
while taking into account context information and solving optimization problems. For instance,
authors in [3] used reinforcement learning to determine phase shift and amplitude of each antenna
element with the purpose to optimize the aggregated throughput of the antennas. In [62], authors
used reinforcement learning to improve the URLLC energy efficiency and delay tolerant services
through resource allocation. In [73], the authors also considered a URLLC service but this time they
worked on optimizing packet scheduling of a multipath protocol using reinforcement learning. In [57],
the authors adopted reinforcement learning for network slicing in RAN in an attempt to optimize
resource utilization. To handle the cache allocation problem in multiple RRHs and multiple BBU pools,
the authors in [55] used reinforcement learning to maximize the cache hit rate and maximize the
cache capacity. In [77], reinforcement learning was used to configure indoor small cell networks
in order to optimize opinion score (MOS) and user QoE. Finally, in [2], reinforcement learning was
used to select radio parameters and optimize different metrics according with the scenario addressed.

3.2.3. Unsupervised Learning
      We examined four articles that used unsupervised learning to train the models proposed.
In [61], the authors proposed a hybrid approach with both supervised and unsupervised learning to
train the model with the purpose to determine an approximate solution for optimal joint resource
allocation strategy and energy consumption. The authors in [30] also used a hybrid learning approach,
combining supervised and unsupervised learning to train the model in order to identify faults and
false alarms among alarm information considering single link connections. In [25], the authors trained
a deep learning model through unsupervised learning to map constellation mapping and demapping
of symbols on each subcarrier in an OFDM system, while minimizing the BER. In [24], an unsupervised
deep learning model was proposed to represent a MU-SIMO system. Its main purpose was to reduce
the difference between the signal transmitted and the signal received.

3.3. What Are the Main Deep Learning Techniques Used in 5G Scenarios?
      Figure 3 shows the common deep learning techniques used to address 5G problems in
the literature. Traditional neural networks with fully connected layers is the deep learning technique
that most appears in the works (reaching 24 articles), followed by long short-term memory (LSTM)
(with 14 articles), and convolutional neural network (CNN) (adopted by only 9 articles).

Algorithms 2020, 13, 208                                                                         14 of 34

                           Figure 3. Most common deep learning techniques for 5G.

3.3.1. Fully Connected Models
       Most of the works that used fully connected layers addressed problems related to the
physical medium in 5G systems [2,11,15–17,21,22,24,26,56,60,62–65,68,72,74,76]. This can be justified
because physical information usually can be structured (e.g., CSI, channel quality indicator (CQI),
radio condition information, etc.). In addition, these works did not consider more complex data,
such as historical information. It is understandable that the 5G physical layer receives such attention.
It is the scene of a number of new technologies such as mmWave, MIMO and antenna beamforming.
These are very challenging technologies that require real time fine tuning.
       However, although fully connected layers were not designed to deal with sequential data,
some works found in this systematic review proposed models based on time series. In [10,41],
the authors considered real data of cellular networks such as Internet usage, SMS, and calls.
Although the dataset has spatio-temporal characteristics, the authors extracted features to compose a
new input for the deep learning model. In [52], the authors proposed a fully connected model to deal
with user coordinate location data. In this work both fully connected and LSTM models were proposed
for comparison and the temporal aspect of dataset was maintained. In [66], the authors adopted a
dataset composed of historical data records for urban and rural areas. Unfortunately, the paper did
not provide more details about the data used, but a deep learning model composed of fully connected
layers was used to process this data.
       In [73], a fully connected model was used with a reinforcement learning algorithm. In this work,
the open source public Mininet simulator was used to create a network topology (the environment)
in order to train the agent. Subsequently, the deep learning model was used to chose the best action
according with the environment.

Algorithms 2020, 13, 208                                                                               15 of 34

3.3.2. Recurrent Neural Networks
     As highlighted in [9], a recurrent neural network (RNN) is able to deal with sequential data,
such as time series, speech and language. It is due to its capacity for, given an element in a sequence,
storing information of past elements. Therefore, one work used RNN [17] and several others used
RNN variations (such as LSTM [87–90]) to deal with sequential data.
     In [70], Zhang et al. proposed a digital cancellation scheme to eliminate linear and non-linear
interference based on deep learning. The deep learning model receives a signal and the custom loss
function represents the residual interference between the real and estimated self-interference signal.
This model was based on RNN but with a custom memory unit.
     In [17], authors used data from channel estimations using the ray tracing propagation software.
The data was processed using traditional RNN layers to capture the time-varying nature of CSI.
Similarly, several works adopted deep learning models with LSTM layers. This can be justified as
LSTM is widely used in the literature to process sequential data.
     The authors in [45,46] used the same dataset to train their models (Telecom Italia, see the
Section 3.4). In [46], a multivariate LSTM model was proposed to learn the temporal and spatial
correlation among the base station traffic and make an accurate forecast. In [45], an LSTM model
was proposed to extract temporal features of mobile Internet traffic and predict Internet flows for
cellular networks.
     In [52], an LSTM model was suggested to deal with another open dataset in order to
predict handover. The dataset is composed of historical location of the users, and the model exploits
the long-term dependencies and temporal correlation of data.
     In [48], the authors proposed an LSTM model for handling historical data of traffic volume.
The model was constructed to predict real time traffic of base stations in order to give relevant
information to increase the accuracy of the offloading scheme proposed.
     In [47], Zhou et al. also proposed an LSTM model to predict traffic in base stations in order to
avoid flow congestion in 5G ultra dense networks. Uplink and downlink flows data were considered
as input for the model. With the predicted data, it is possible to allocate more resources for uplink or
downlink channels accordingly.
     In [7], an LSTM model was proposed to make online CSI prediction. The model explored the
temporal dependency of historical data of frequency band, location, time, temperature, humidity,
and weather. The dataset was measured through experiments within a testbed.
     In [58], a variation of LSTM called X-LSTM was proposed in order to predict a metric called REVA,
which measures the amount of PRBs available in a network slice. X-LSTM is based on X-11, which is
an interative process that decomposes the data into seasonal patterns. X-LSTM uses different LSTM
models to evaluate different time scales of data. “It filters out higher order temporal patterns and uses the
residual to make additional predictions on data with a shorter time scale” [58]. The input data of the model is
the historical data of PRB measured through a testbed, where the REVA metric was calculated.
     In [71], the authors represented the memory aspect PA using a biLSTM model. The authors
established a bridge between the theoretical formalism of PA behavior and the characteristic
of biLSTM models to consider both forward an backward temporal aspect of the input data
(baseband measurements using a testbed).
     In [35,36,51,59], the authors used LSTM to deal with sequential data generated through simulation.
In [59], the LSTM model was used to predict if a new network slice can be allocated given the sequential
data of allocated resources and channel conditions. In [51], the LSTM model was used to evaluate
historical signal condition in order to classify event in either handover fail or success in advance.
In [36], the developed LSTM model was applied to learn the users mobility pattern in order to predict
their movement trends in the future based on historical trajectories. In [35], the authors used LSTM to
predict position of users based on historical beamformed fingerprint data (considering the presence o
buildings in a scenario generated through simulations).

Algorithms 2020, 13, 208                                                                          16 of 34

      The work presented in [26] proposed an LSTM model to represent the coding/decoding schema
considering a hybrid approach to support polar codes. Unfortunately, the authors did not describe the
data used to train their model.
      In [27,43], gated recurrent unit (GRU) layers are considered to deal with sequential data. In [43],
real ISP data is used to train the model. The authors used a testbed to create the dataset composed
of GPON (ZTE C320) to demonstrate the fronthaul, while midhaul and backhaul are enabled by the
MPLS feature of SDN switches. Details about the dataset used in [27] are not provided.

3.3.3. CNN
     CNN models are created to deal with data that come from multiple arrays or multivariate arrays
and extract relevant features from them. In other words, the convolution layer is applied to process
data with different dimensionality: 1D for signals and sequences, 2D for images or audio spectrograms,
and 3D for video or volumetric images [9]. As a result, this layer was typically used to deal with
several types of data in the works found in this systematic review.
     The works in [29,34,35,75], presented the input data for the CNN models as an image form in order
to take advantage of the natural features of the convolutions applied by the CNN layers. Both temporal
and geographical aspects were considered in the works presented in [6,44,45]. These are relevant plans
since the metrics have different behavior according to the time of the day and the base station location.
As a result, these works used CNN to take into consideration temporal and space aspects at the same
time and extract relevant joint patterns. The works presented in [7] used CNN models and considered
several aspects that affect the CSI as input for the models such as frequency band, location, time,
temperature, humidity, and weather. The authors considered 1D and 2D convolutions in order to
extract frequency representative vector from CSI information.
     A separate work used a special architecture of CNN called ResNet [19]. This architecture was
proposed to solve the notorious problem of a vanishing/exploding gradient. The main difference
offered by the ResNet architecture is that a shortcut connection is added every two or three layers in
order to skip the connections and reuse activation from a previous layer until the adjacent layer learns
its weights. This architecture was used to process modulated frequency-domain sequence data for the
purpose of channel estimation.
     In addition to the LSTM and CNN models, the authors proposed a model named a temporal
convolutional network (TCN) in [35]. Unlike the other models, the TCN architecture considers the
temporal dependency in a more accurate way. The interested reader may find out more detailed
information TCN by consulting [91].
     In [26], besides describing a fully connected layers and an LSTM models, the authors also proposed
a CNN model for use with LSTM to represent the coding/decoding schema as convolution functions.

3.3.4. DBN
      Deep belief networks (DBNs) are attractive for problems with few labeled data and a large amount
of unlabeled ones. This is mainly due to the fact that during the training process, unlabeled data
are used for training the model and the labeled data are used for fine-tuning the entire network [92].
Therefore, this deep learning technique combines both supervised and unsupervised learning during
the training process.
      For instance, the works presented in [38,40] used a dataset composed of several network flows of
computers infected with botnets. The DBN model was used to detect traffic anomalies.
      Similarly, in [61], the authors proposed a DBN model where the dataset used consisted of the
channel coefficients and the respective optimal downlink resource allocation solution.
      In [30], another DBN model was trained using a hybrid approach (supervised and unsupervised)
for fault location on optical fronthalls. The dataset used was taken from a real management system of
a network operator, and consists of link faults events.